Development of protocols to estimate maximal oxygen consumption and body composition in individuals with spinal cord injury (884.27)

Abstract

Cardiovascular complications and obesity are common secondary complications after spinal cord injury (SCI). The measurement of VO2max during graded exercise test is the gold standard to evaluate cardiovascular fitness. Due to the complexity and cost of VO2max measurements, regression equations to estimateVO2max from submaximal tests based on heart rate have been developed. Due to sympathetic impairments secondary to SCI heart rate may not be an accurate tool to use to predict VO2 max in SCI. The aim of this study was to validate a submaximal test based on Ratings of Perceived Exertion (RPE) to predict VO2max for people with SCI. A second aim of this study was to validate current skinfold equations to estimate percentage of body fat (%fat) in this population. Methods: 10 able‐bodied and 12 SCI individuals participated in this study. The standardized test protocols for VO2 max were performed on a total body recumbent stepper (NuStep T4 ergometer). Analysis of the expired gases occurred every 10 seconds. Heart rate was recorded using a Polar heart rate monitor and 3‐lead ECG. RPE was recorded using the Borg 6‐20 scale. Estimation of %fat by skinfold measurements in supine position were compared with values obtained by DXA scans. Results: There was a strong positive correlation between predicted and observed VO2max in able‐bodied subjects using RPE (ρ = 0.86; p蠄0.05) and RPE + watts (ρ = 0.88; p蠄0.05). SCI subjects were able to perform both, the maximal and submaximal protocols, with a cadence of 80 steps per minute. Current validated skinfolds equations underestimate %fat in SCI individuals. Conclusions: The results of this study show promise for the development of submaximal RPE‐based protocols for the prediction of VO2max in this population. Future directions include the examination of protocols ending at RPE 13 and RPE 15 as well as to develop prediction equations for a larger group of individuals with varying levels and severity of SCI. New prediction equations to estimate %fat for SCI need to be developed.